The World Bank — in collaboration with the United Nations Conference on Trade and Development (UNCTAD) and in consultation with organizations such as International Trade Center, United Nations Statistical Division (UNSD) and the World Trade Organization (WTO) — developed the World Integrated Trade Solution (WITS).

The Export of Value Added (EVA) dataset illustrates the strength of economy- wide linkages. It provides data on how value added structures and services linkages to trade have evolved over time. Thanks to repeated updating of the GTAP dataset, we have data for both cross border linkages in recent years, and how these have changed since the early 1990s. This serves as the basis for the database, which builds on Christen, Francois, and Hoekman (2012) and Francois, Manchin, and Tomberger (2012). We work with a panel of global input-output data (a set of global social accounting matrices spanning intermittent years from 1992 to 2011) that covers not only key OECD economies, but also a range of developing countries as well.
Sector_GMatrix:
This matrix contains the total domestic value added based on linkages. Depending whether rows or columns are considered its sum corresponds to forward (row) or backward (colunn) linkages. Thus reading a row for a given sector (sector presented on the y-axis) provides information about how much this sector went into each sector (on the x-axis) as inputs
DomVAshare:
This vector denotes the domestic share of value added of gross value of output per sector.
GXshare:
Denotes the share of each sector in total exports per country based on the gross value of exports.
DXshare:
Denotes the share of each sector’s exports of total exports per country based on direct value added, ignoring linkages.
VXsharefwd:
Denotes the total value added in exports based on forward linkages per sector and country.
VXsharebwd:
Denotes the total value added in exports based on backward linkages. It is obtained by taking the column-sums of matrix H.

The dataset provides a consolidated and reconciled version of multiple sources of bilateral
trade data. Its advantages over the original source data are that it provides broader
coverage based on mirror flows, reconciliation of aggregate with underlying flows, and
consolidation (allowing for broader coverage than offered by source data). One weakness,
inherent in all available data of this type, is that even with mirror flows, a substantial share
of South-South trade is unreported. As such, while we can recover North-South exports
from mirror flows, we cannot recover all unreported bilateral flows. The scale of the
problem can be gauged by comparing trade with the world with bilateral flows in the
database.
Notes:
• values are in millions of current US dollars
• Because of the apparent mixing of zero and missing by source agencies, we have opted
to use missing, or “.”, for reported zero and missing flows.
• total with world is the greater of reported total with world, or aggregate of bilateral
flows
• region XWD holds difference between all bilateral flows and global (trade with world)
total. It is the sum of flows with missing partners. This means XWD holds identified
flows without a partner. It does not hold flows that are totally unreported.
• all unreported REP:PAR:BOP:YEAR combinations, meaning that do not even appear in
the dataset, can be safely assumed to be missing. By this, we mean there is no
reported source for these flows, and the countries-product-year combination does
not even occur in any of the underlying source data.

In Tariff Cuts module, users can cut the applied tariff rates using prescribed formulas. Available formulas include specification of new rate or new maximum rate, linear percentage cut or Swiss formula. Any number of different formulas (or same formula with different parameters) may be applied for different products and countries. Both pre- and post-tariff cut rates are reported for every importer-exporter combination and for each product at HS 6-digit level.